In digital image processing, for example computational photography and image processing, computer-based algorithms provide for manipulations in digital images. For example, digital image processing allows for features such as classification, feature extraction, or pattern recognition to be carried out in digital imaging applications. Further, many applications in the field of digital image processing require edge-preserving smoothing. Such applications can include de-noising of images, tone mapping of high dynamic range (HDR) images, fusion of differently exposed low dynamic range (LDR) images, detail enhancement via multi-light images, texture transfer, and single image haze removal.
Image processing to address the edge-preserving smoothing problem can be carried out with digital filters. Digital filters are classified as either global or local, where a global digital filter processes each using all pixels of an input image, while a local digital filter surveys every pixel in the image using pixels from a neighborhood of the pixel. Similarly, the edge-preserving smoothing problem can be addressed globally or locally. Global optimization-based approaches often yield high state-of-the-art quality, but require an expensive computational cost. Local filtering methods are generally simpler, but are typically unable to preserved sharp edges as well as that achieved through the global optimization based filters.
It is thus desired for an alternative digital image processing solution which may provide a result with quality as from a global optimization approach, but without the heavy computational cost required.